Optimal and efficient crossover designs when subject effects are random

A. S. Hedayat, John Stufken, Min Yang

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations


Most studies on optimal crossover designs are based on models that assume subject effects to be fixed effects. In this article we identify and study optimal and efficient designs for a model with random subject effects. With the number of periods not exceeding the number of treatments, we find that totally balanced designs are universally optimal for treatment effects in a large subclass of competing designs. However, in the entire class of designs, totally balanced designs are in general not optimal, and their efficiency depends on the ratio of the subject effects variance and the error variance. We develop tools to study the efficiency of totally balanced designs and to identify designs with higher efficiency.

Original languageEnglish (US)
Pages (from-to)1031-1038
Number of pages8
JournalJournal of the American Statistical Association
Issue number475
StatePublished - Sep 2006


  • Fisher information matrix
  • Mixed-effects model
  • Totally balanced design
  • Universal optimality

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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